SoroushVahidi/scidocs huggingface dataset:
A Database for Handwritten Text Recognition Research
(1994)

We propose in this correspondence a new method to perform two-class clustering of 2-D data in a quick and automatic way by preserving certain features of the input data. The method is analytical, deterministic, unsupervised, automatic, and noniterative. The computation time is of order n if the data size is n, and hence much faster than any other method which requires the computation of an n-by-n dissimilarity matrix. Furthermore, the proposed method does not have the trouble of guessing initial values. This new approach is thus more suitable for fast automatic hierarchical clustering or any other fields requiring fast automatic two-class clustering of 2-D data. The method can be extended to cluster data in higher dimensional space. A 3-D example is included.
Continuous speech recognition using hidden Markov models
(1990)

The use of hidden Markov models (HMMs) in continuous speech recognition is reviewed. Markov models are presented as a generalization of their predecessor technology, dynamic programming. A unified view is offered in which both linguistic decoding and acoustic matching are integrated into a single, optimal network search framework. Advances in recognition architectures are discussed. The fundamentals of Viterbi beam search, the dominant search algorithm used today in speed recognition, are presented. Approaches to estimating the probabilities associated with an HMM model are examined. The HMM-supervised training paradigm is examined. Several examples of successful HMM-based speech recognition systems are reviewed.<<ETX>>
A framework analysis of the open source software development paradigm
(2000)

Open Source Software (OSS) has become the subject of much commercial interest of late. Certainly, OSS seems to hold much promise in addressing the core issues of the software crisis, namely that of software taking too long to develop, exceeding its budget, and not working very well. Indeed, there have been several examples of significant OSS success stories—the Linux operating system, the Apache web server, the BIND domain name resolution utility, to name but a few. However, little by way of rigorous academic research on OSS has been conducted to date. In this study, a framework was derived from two previous frameworks which have been very influential in the IS field, namely that of Zachman’s IS architecture (ISA) and Checkland’s CATWOE framework from Soft Systems Methodology (SSM). The resulting framework is used to analyze the OSS approach in detail. The potential future of OSS research is also discussed.
On pricing discrete barrier options using conditional expectation and importance sampling Monte Carlo
(2008)

Estimators for the price of a discrete barrier option based on conditional expectation and importance sampling variance reduction techniques are given. There are erroneous formulas for the conditional expectation estimator published in the literature: we derive the correct expression for the estimator. We use a simulated annealing algorithm to estimate the optimal parameters of exponential twisting in importance sampling, and compare it with a heuristic used in the literature. Randomized quasi-Monte Carlo methods are used to further increase the accuracy of the estimators. c © 2007 Elsevier Ltd. All rights reserved.
On the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX)
(2005)

The first energetically autonomous lower extremity exoskeleton capable of carrying a payload has been demonstrated at U.C. Berkeley. This paper summarizes the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). The anthropomorphically-based BLEEX has seven degrees of freedom per leg, four of which are powered by linear hydraulic actuators. The selection of the degrees of freedom and their ranges of motion are described. Additionally, the significant design aspects of the major BLEEX components are covered.
Five Styles of Customer Knowledge Management , and How Smart Companies Use Them To Create Value
(2002)

Corporations are beginning to realize that the proverbial ‘if we only knew what we know’ also includes ‘if we only knew what our customers know.’ The authors discuss the concept of Customer Knowledge Management (CKM), which refers to the management of knowledge from customers, i.e. knowledge resident in customers. CKM is contrasted with knowledge about customers, e.g. customer characteristics and preferences prevalent in previous work on knowledge management and customer relationship management. Five styles of CKM are proposed and practically illustrated by way of corporate examples. Implications are discussed for knowledge management, the resource based view, and strategy process research.  2002 Elsevier Science Ltd. All rights reserved
Rule Based Part of Speech Tagging of Sindhi Language
(2010)

Part of Speech (POS) tagging is a process of assigning correct syntactic categories to each word in the text. Tag set and word disambiguation rules are fundamental parts of any POS tagger. No work has hitherto been published of tag set in Sindhi language. The Sindhi lexicon for computational processing is also not available. In this study, the tag set for Sindhi POS, lexicon and word disambiguation rules are designed and developed. The Sindhi corpus is collected from a comprehensive Sindhi Dictionary. The corpus is based on the most recent available vocabulary used by local people. In this paper, preliminary achievements of rule based Sindhi Part of Speech (SPOS) tagger are presented. Tagging and tokenization algorithms are also designed for the implementation of SPOS. The outputs of SPOS are verified by Sindhi linguist. The development of SPOS tagger may have an important milestone towards computational Sindhi language processing.
gNUFFTW : Auto-Tuning for High-Performance GPU-Accelerated Non-Uniform Fast Fourier Transforms by Teresa
(2017)

Non-uniform sampling of the Fourier transform appears in many important applications such as magnetic resonance imaging (MRI), optics, tomography and radio interferometry. Computing the inverse often requires fast application of the non-uniform discrete Fourier transform (NUDFT) and its adjoint operation. Non-Uniform Fast Fourier Transform (NUFFT) methods, such as gridding/regridding, are approximate algorithms which often leverage the highly-optimized Fast Fourier Transform (FFT) and localized interpolations. These approaches require selecting several parameters, such as interpolation and FFT grid sizes, which affect both the accuracy and runtime. In addition, different implementations lie on a spectrum of precomputation levels, which can further speed up repeated computations, with various trade-offs in planning time, execution time and memory usage. Choosing the optimal parameters and implementations is important for performance speed, but difficult to do manually since the performance of NUFFT is not well-understood for modern parallel processors. Inspired by the FFTW library, we demonstrate an empirical auto-tuning approach for the NUFFT on General Purpose Graphics Processors Units (GPGPU). We demonstrate order-of-magnitude speed improvements with autotuning compared to typical default choices. Our auto-tuning is implemented in an easy to use proof-of-concept library called gNUFFTW, which leverages existing open-source NUFFT packages, cuFFT and cuSPARSE libraries, as well as our own NUFFT implementations for high performance. Keywords—non-uniform, non-Cartesian, FFT, NUFFT, GPU, auto-tuning, image processing.
Automatic Medical Concept Extraction from Free Text Clinical Reports, a New Named Entity Recognition Approach
(2017)

Actually in the Hospital Information Systems, there is a wide range of clinical information representation from the Electronic Health Records (EHR), and most of the information contained in clinical reports is written in natural language free text. In this context, we are researching the problem of automatic clinical named entities recognition from free text clinical reports. We are using Snomed-CT (Systematized Nomenclature of Medicine – Clinical Terms) as dictionary to identify all kind of clinical concepts, and thus the problem we are considering is to map each clinical entity named in a free text report with its Snomed-CT unique ID. More in general, we are developed a new approach for the named entity recognition (NER) problem in specific domains, and we have applied it to recognize clinical concepts in free text clinical reports. In our approach we apply two types of NER approaches, dictionary-based and machine learning-based. We use a specific domain dictionary-based gazetteer (using Snomed-CT to get the standard clinical code for the clinical concept), and the main approach that we introduce is using a unsupervised shallow learning neural network, word2vec from Mikolov et al., to represent words as vectors, and then making the recognition based on the distance between candidates and dictionary terms. We have applied our approach on a Dataset with 318.585 clinical reports in Spanish from the emergency service of the Hospital “Rafael Méndez” from Lorca (Murcia) Spain, and preliminary results are encouraging. Key-Words: Snomed-CT, word2vec, doc2vec, clinical information extraction, skipgram, medical terminologies, search semantic, named entity recognition, ner, medical entity recognition
Femtosecond-Long Pulse-Based Modulation for Terahertz Band Communication in Nanonetworks
(2014)

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. Nanonetworks are the enabling technology of long-awaited applications such as advanced health monitoring systems or high-performance distributed nano-computing architectures. The peculiarities of novel plasmonic nano-transceivers and nano-antennas, which operate in the Terahertz Band (0.1-10 THz), require the development of tailored communication schemes for nanonetworks. In this paper, a modulation and channel access scheme for nanonetworks in the Terahertz Band is developed. The proposed technique is based on the transmission of one-hundred-femtosecond-long pulses by following an asymmetric On-Off Keying modulation Spread in Time (TS-OOK). The performance of TS-OOK is evaluated in terms of the achievable information rate in the single-user and the multi-user cases. An accurate Terahertz Band channel model, validated by COMSOL simulation, is used, and novel stochastic models for the molecular absorption noise in the Terahertz Band and for the multi-user interference in TS-OOK are developed. The results show that the proposed modulation can support a very large number of nano-devices simultaneously transmitting at multiple Gigabits-per-second and up to Terabits-per-second, depending on the modulation parameters and the network conditions.
Flow map layout
(2005)

Cartographers have long used flow maps to show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network. The advantage of flow maps is that they reduce visual clutter by merging edges. Most flow maps are drawn by hand and there are few computer algorithms available. We present a method for generating flow maps using hierarchical clustering given a set of nodes, positions, and flow data between the nodes. Our techniques are inspired by graph layout algorithms that minimize edge crossings and distort node positions while maintaining their relative position to one another. We demonstrate our technique by producing flow maps for network traffic, census data, and trade data.
Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs
(2017)

We describe a Deep Learning approach to modeling the relevance of a document’s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document’s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search.
Electrically small omni-directional antenna of circular polarization
(2012)

A compact omni-directional antenna of circular polarization (CP) is presented for 2.4 GHz WLAN access-point applications. The antenna consists of four bended monopoles and a feeding network simultaneously exciting these four monopoles. The electrical size of the CP antenna is only λ<sub>0</sub>/5×λ<sub>0</sub>/5×λ<sub>0</sub>/13. The impedance bandwidth (|S<sub>11</sub>|<;-10 dB) is 3.85% (2.392 GHz to 2.486 GHz) and the axial ratio in the azimuth plane is lower than 0.5 dB in the operating band.
Avoiding monotony: improving the diversity of recommendation lists
(2008)

The primary premise upon which top-N recommender systems operate is that similar users are likely to have similar tastes with regard to their product choices. For this reason, recommender algorithms depend deeply on similarity metrics to build the recommendation lists for end-users.
 However, it has been noted that the products offered on recommendation lists are often too similar to each other and attention has been paid towards the goal of improving diversity to avoid monotonous recommendations.
 Noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem. We explore a solution strategy to this optimization problem by relaxing it to a trust-region problem.This leads to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution. We apply this approach to the top-N prediction problem, evaluate the system performance on the Movielens dataset and compare it with a standard item-based top-N algorithm. A new evaluation metric ItemNovelty is proposed in this work. Improvements on both diversity and accuracy are obtained compared to the benchmark algorithm.
Small-signal modeling of pulse-width modulated switched-mode power converters
(1988)

A power processing system is required to convert electrical energy from one voltage, current, or frequency to another with, ideally, 100-percent efficiency, together with adjustability of the conversion ratio. The allowable elements are switches, capacitors, and magnetic devices. Essential to the design is a knowledge of the transfer functions, and since the switching converter is nonlinear, a suitable modeling approach is needed. This paper discusses the state-space averaging method, which is an almost ideal compromise between accuracy and simplicity. The treatment here emphasizes the motivation and objectives of modeling by equivalent circuits based upon physical interpretation of state-space averaging, and is limited to pulse-width modulated dc-to-dc converters.
Software transformations to improve malware detection
(2007)

Malware is code designed for a malicious purpose, such as obtaining root privilege on a host. A malware detector identifies malware and thus prevents it from adversely affecting a host. In order to evade detection, malware writers use various obfuscation techniques to transform their malware. There is strong evidence that commercial malware detectors are susceptible to these evasion tactics. In this paper, we describe the design and implementation of a malware transformer that reverses the obfuscations performed by a malware writer. Our experimental evaluation demonstrates that this malware transformer can drastically improve the detection rates of commercial malware detectors.
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic
(2015)

Marr's levels of analysis-computational, algorithmic, and implementation-have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call "resource-rational analysis."
True upper bounds for Bermudan products via non-nested Monte
(2007)

We present a generic non-nested Monte Carlo procedure for computing true upper bounds for Bermudan products, given an approximation of the Snell envelope. The pleonastic “true” stresses that, by construction, the estimator is biased above the Snell envelope. The key idea is a regression estimator for the Doob martingale part of the approximative Snell envelope, which preserves the martingale property. The so constructed martingale can be employed for computing tight dual upper bounds without nested simulation. In general, this martingale can also be used as a control variate for simulation of conditional expectations. In this context, we develop a variance reduced version of the nested primal-dual estimator (Andersen and Broadie, 2004). Numerical experiments indicate the efficiency of the proposed algorithms.
Forecasting and Trading Currency Volatility : An Application of Recurrent Neural Regression and Model Combination by
(2001)

In this paper, we examine the use of GARCH models, Neural Network Regression (NNR), Recurrent Neural Network (RNN) regression and model combinations for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR/RNN models and the model combination results are benchmarked against the simpler GARCH alternative. The idea of developing a nonlinear nonparametric approach to forecast FX volatility, identify mispriced options and subsequently develop a trading strategy based upon this process is intuitively appealing. Using daily data from December 1993 through April 1999, we develop alternative FX volatility forecasting models. These models are then tested out-of-sample over the period April 1999-May 2000, not only in terms of forecasting accuracy, but also in terms of trading efficiency: In order to do so, we apply a realistic volatility trading strategy using FX option straddles once mispriced options have been identified. Allowing for transaction costs, most trading strategies retained produce positive returns. RNN models appear as the best single modelling approach yet, somewhat surprisingly, model combination which has the best overall performance in terms of forecasting accuracy, fails to improve the RNN-based volatility trading results. Another conclusion from our results is that, for the period and currencies considered, the currency option market was inefficient and/or the pricing formulae applied by market participants were inadequate. * Christian Dunis is Girobank Professor of Banking and Finance at Liverpool Business School and Director of CIBEF (E-mail: cdunis@globalnet.co.uk). The opinions expressed herein are not those of Girobank. ** Xuehuan Huang is an Associate Researcher with CIBEF. *** CIBEF – Centre for International Banking, Economics and Finance, JMU, John Foster Building, 98 Mount Pleasant, Liverpool L3 5UZ. We are grateful to Professor Ken Holden and Professor John Thompson of Liverpool Business School for helpful comments on an earlier version of this paper.
Neural systems responding to degrees of uncertainty in human decision-making.
(2005)

Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.
Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
(2017)

Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management.
1.5MVA grid-connected interleaved inverters using coupled inductors for wind power generation system
(2013)

In this paper, grid-connected interleaved voltage source inverters for PMSG wind power generation system with coupled inductors is introduced. In parallel operation, the undesirable circulating current flows between inverters. There are some differences in circulating currents according to source configuration. It is mathematically analyzed and simulated for the verification of analysis. In case of interleaved inverters, the uncontrollable circulating current in switching frequency level flows between inverter modules regardless of source configuration such as common or separated if filter inductance which is placed between inverters is significantly low. In order to suppress high frequency circulating current, the coupled inductors are employed in each phase. A case of 1.5MVA grid-connected interleaved inverters using coupled inductors prototype for the PMSG wind power generation Back To Back converters in parallel are introduced and experimentally verified the proposed topology.